Peak To Gene Assignment

Prepare referece

#!/bin/bash

#SBATCH --job-name=intGenes_combfilterPeaksOppStrand
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=intGenes_combfilterPeaksOppStrand.out
#SBATCH --error=intGenes_combfilterPeaksOppStrand.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

bedtools intersect -wa -wb -sorted -S -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand.bed

This results in peaks being mapped to multiple genes. I want to use a method where I look for the closest end of transcript to each peak then use that gene for the assignment. This would mean each peak is assigned to one gene.

Create a python script to process the NCBI file. I want protien coding transcript ends with the associated gene names. Original file: ncbiRefSeq.txt

• Column 2 transcript name
• Column 13 gene name
• NM is protein coding

EndOfProCodTrans.py

def main(inF, outF):
  infile= open(inF, "r")
  fout = open(outF,'w')
  for line in infile:
      linelist=line.split()
      transcript=linelist[1]
      transcript_id=transcript.split("_")[0]
      if transcript_id=="NM":
          chr=linelist[2][3:]
          strand=linelist[3] 
          gene= linelist[12]
          if strand == "+" :
              end = int(linelist[7])
              end2= end - 1
              fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end2, end, transcript,gene, strand))
          if strand == "-":
              end= int(linelist[4])
              end2= end + 1
              fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end, end2, transcript,gene, strand))


if __name__ == "__main__":
    inF = "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq.txt"
    outF= "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes.txt"
    main(inF, outF)

Find closest gene to each peak

bedtools closest

-A peaks /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -B transcript file /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt -S (opposite strand) -D b (give distance wrt to gene strand)

#!/bin/bash

#SBATCH --job-name=TransClosest2End
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TransClosest2End.out
#SBATCH --error=TransClosest2End.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3 

source activate three-prime-env


bedtools closest -S -D b -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b  /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed

I will take a look at this file in R then I will process the file in python.

names=c("PeakChr", "PeakStart", "PeakEnd", "PeakName","PeakScore", "PeakStrand", "GeneChr", "GeneStart", "GeneEnd", "Transcript", "GeneScore", "GeneStrand", "Distance" )
peak2transDist=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed", col.names = names, stringsAsFactors = F, header=F)

ggplot(peak2transDist, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 4362 rows containing non-finite values (stat_density).

Past versions of unnamed-chunk-6-1.png

peak2transDist0=peak2transDist %>% filter(Distance==0)
nrow(peak2transDist0)

[1] 4362

peak2transDist200=peak2transDist %>% filter(abs(Distance)<200)
nrow(peak2transDist200)

[1] 23778

summary(peak2transDist$Distance)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-5523243   -57698   -12830   -23711     3373  5592124 

try adding the no ties flag -t first.

peak2transDist_noties=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed", col.names = names, stringsAsFactors = F, header=F)
ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 2044 rows containing non-finite values (stat_density).

Past versions of unnamed-chunk-8-1.png

peak2transDist0_noT=peak2transDist_noties%>% filter(Distance==0)
nrow(peak2transDist0_noT)

[1] 2044

peak2transDist200_noT=peak2transDist_noties %>% filter(abs(Distance)<200)
nrow(peak2transDist200_noT)

[1] 10488

summary(peak2transDist$Distance)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-5523243   -57698   -12830   -23711     3373  5592124 

ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_histogram(binwidth = .5) + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 2044 rows containing non-finite values (stat_bin).

Past versions of unnamed-chunk-9-1.png

Looking at this visually suggests that we have way too many peaks. I want to compare the peak score which is related to the coverage to the abs(distace)

ggplot(peak2transDist_noties, aes(y=PeakScore, x=abs(Distance + 1))) + geom_point() + scale_x_log10() + scale_y_log10() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') 

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Transformation introduced infinite values in continuous x-axis

Past versions of unnamed-chunk-10-1.png

Alternatively let me try to remove low peak score values.

allPeakplot=ggplot(peak2transDist_noties, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Distance all peaks to gene end") + annotate("text", label=nrow(peak2transDist_noties), x=10, y=.4)

peak2transDist_score500=peak2transDist_noties%>% filter(PeakScore>500)
score500plot=ggplot(peak2transDist_score500, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 500") + annotate("text", label=nrow(peak2transDist_score500), x=10, y=.4)


peak2transDist_score200=peak2transDist_noties%>% filter(PeakScore>200)
score200plot=ggplot(peak2transDist_score200, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 200") + annotate("text", label=nrow(peak2transDist_score200), x=10, y=.4)


peak2transDist_score100=peak2transDist_noties%>% filter(PeakScore>100)
score100plot=ggplot(peak2transDist_score100, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 100") + annotate("text", label=nrow(peak2transDist_score100), x=10, y=.4)

peak2transDist_score50=peak2transDist_noties%>% filter(PeakScore>50)
score50plot=ggplot(peak2transDist_score50, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 50")+ annotate("text", label=nrow(peak2transDist_score50), x=10, y=.4)

peak2transDist_score20=peak2transDist_noties%>% filter(PeakScore>20)
score20plot=ggplot(peak2transDist_score20, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 10")+ annotate("text", label=nrow(peak2transDist_score20), x=10, y=.4)

distance2peak_all=plot_grid(allPeakplot,score20plot,score50plot,score100plot,score200plot, score500plot)

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 662 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 431 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 327 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 234 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 150 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 78 rows containing non-finite values (stat_density).

ggsave("../output/plots/QC_plots/distance2peak_all.png", distance2peak_all, width=8, height=6)

Call QTLS with this assignment

I am gonig to use this assignment method to call QTLs. The bed file I will make the phenotypes from is

• Peak CHR
  
• Peak Start
  
• Peak End
  
• Peak Name
• Peak Score
  
• Gene strand
• Gene/transcript name

in the filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed file this is

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $12 "\t" $10}' filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed > filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.bed

less /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SA | tr ":" "-" > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

Make this an SAF file with the correct peak ID. bed2saf_peaks2trans.py

from misc_helper import *

fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed"):
    chrom, start, end, name, score, strand, gene = ln.split()
    name_i=int(name)
    start_i=int(start)
    end_i=int(end)
    gene_only=gene.split("-")[1]
    ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom, start_i, end_i, strand, gene_only)
    fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
fout.close()

Run feature counts:
ref_gene_peakTranscript_fc_TN.sh

#!/bin/bash

#SBATCH --job-name=ref_gene_peakTranscript_fc_TN
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakTranscript_fc_TN.out
#SBATCH --error=ref_gene_peakTranscript_fc_TN.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2

Fix the headers:

• fix_head_fc_opp_transcript_tot.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

• fix_head_fc_opp_transcript_nuc.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

Create file IDS:

• create_fileid_opp_transcript_total.py

fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

• create_fileid_opp_transcript_nuc.py

fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

(remove top line)

awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt >  /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt




awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt > /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt

Make Phenotypes:

• makePhenoRefSeqPeaks_Transcript_Total.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

• makePhenoRefSeqPeaks_Transcript_Nuclear.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA)  
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

I can run these with the following bash script:

• run_makePhen_sep_Transcript.sh

#!/bin/bash

#SBATCH --job-name=run_makepheno_sep_trans
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_makepheno_sep_trans.out
#SBATCH --error=run_makepheno_sep_trans.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_Transcript_Total.py  

python makePhenoRefSeqPeaks_Transcript_Nuclear.py  

Prepare for FastQTL

I will do this in the /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/ directory.

module load samtools
#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz 

#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz_prepare.sh

#run for nuclear as well 
gzip  filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz
#load anaconda and env. 
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz_prepare.sh





#keep only 2 PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs

Make a sample list.

• makeSampleList_trascript.py

#make a sample list  

fout = open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt",'w')

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt", "r"):
    bam, sample = ln.split()
    line=sample[:-2]
    fout.write("NA"+line + "\n")
fout.close()

** Manually ** Remove 18500, 19092 and 19193, 18497

Run FastQTL

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_transcript.out
#SBATCH --error=APAqtl_nominal_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

#!/bin/bash


#SBATCH --job-name=APAqtl_permuted_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_permuted_transcript.out
#SBATCH --error=APAqtl_permuted_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_transcript.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_transcript.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)

#!/bin/bash


#SBATCH --job-name=run_APAqtlpermCorrectQQplot_trans
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot_trans.out
#SBATCH --error=run_APAqtlpermCorrectQQplot_trans.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot_trans.R 

Genes with mult peaks-New annotation

peak2transDist_noties_gene = peak2transDist_noties %>% separate(Transcript, c("OnlyTranscript", "Gene"), sep=":") %>% select(PeakName, Gene) %>% group_by(Gene) %>% tally() %>% mutate(onePeak=ifelse(n==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(n > 1, 1, 0 ))

sum(peak2transDist_noties_gene$onePeak==1)

[1] 1591

sum(peak2transDist_noties_gene$multPeaks==1)

[1] 13923

• 1591 Genes have 1 peak. 13923 genes have multiple, 3717 with 0

• In total there are 19231 genes in the annotation.

Plot this:

PeakCategory=c("Zero", "One", "Multiple") 
NumGenes=c(round((19231-sum(peak2transDist_noties_gene$onePeak==1)-sum(peak2transDist_noties_gene$multPeaks==1))/19231, digits = 3), round(sum(peak2transDist_noties_gene$onePeak==1)/19231,digits=3), round(sum(peak2transDist_noties_gene$multPeaks==1)/19231,digits = 3))
GenePeakNumTable=as.data.frame(cbind(PeakCategory,NumGenes))

GenePeakNumTable$NumGenes=as.numeric(as.character(GenePeakNumTable$NumGenes))

lab0=paste("Genes = ", 19231-sum(peak2transDist_noties_gene$onePeak==1)-sum(peak2transDist_noties_gene$multPeaks==1), sep=" ")
lab1=paste("Genes = ", sum(peak2transDist_noties_gene$onePeak==1), sep=" ")
labmult=paste("Genes = ", sum(peak2transDist_noties_gene$multPeaks==1), sep=" ")


GenePeakNumPlot=ggplot(GenePeakNumTable, aes(x="", y=NumGenes, by=PeakCategory, fill=PeakCategory)) + geom_bar(stat="identity",position = "stack") + labs(title="Characterize Protein Coding Genes \n by number of PAS", y="Proportion of genes", x="") + scale_fill_brewer(palette="Paired")  + annotate("text", x="", y= .1, label=lab0) + annotate("text", x="", y= .24, label=lab1)+ annotate("text", x="", y= .6, label=labmult)
#ggsave(GenePeakNumPlot,filename = "../output/plots/PasPerProteinCodingGene.png")

Try this at transcript level:

peak2transDist_noties_transcript = peak2transDist_noties %>% separate(Transcript, c("OnlyTranscript", "Gene"), sep=":") %>% select(PeakName, OnlyTranscript) %>% group_by(OnlyTranscript) %>% tally() %>% mutate(onePeak=ifelse(n==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(n > 1, 1, 0 ))

sum(peak2transDist_noties_transcript$onePeak==1)

[1] 2065

sum(peak2transDist_noties_transcript$multPeaks==1)

[1] 15614

total transcripts: 45024

Evaluate permuted results

tot.perm= read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt",head=T, stringsAsFactors=F)


plot(tot.perm$ppval, tot.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")

Past versions of unnamed-chunk-32-1.png

tot_qtl_10= tot.perm %>% filter(-log10(bh) > 1) %>% nrow()
tot_qtl_10

[1] 118

tot.perm %>% filter(-log10(bh) > 1) %>%  summarise(n_distinct(sid)) 

  n_distinct(sid)
1             112

nuc.perm= read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt",head=T, stringsAsFactors=F)


plot(nuc.perm$ppval, nuc.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")

Past versions of unnamed-chunk-34-1.png

nuc_qtl_10= nuc.perm %>% filter(-log10(bh) > 1) %>% nrow()
nuc_qtl_10

[1] 880

nuc.perm %>% filter(-log10(bh) > 1) %>%  summarise(n_distinct(sid)) 

  n_distinct(sid)
1             831

nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
  x=tot.perm %>% filter(bh < i ) %>% nrow()
  nQTL_tot=c(nQTL_tot, x)
}

FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
  x=nuc.perm %>% filter(bh < i ) %>% nrow()
  nQTL_nuc=c(nQTL_nuc, x)
}

nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")

sigQTLbyFDR=ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")+  scale_color_manual(values=c("#5D478B", "#87CEFF"))
ggsave(plot = sigQTLbyFDR,filename =  "../output/plots/SigQTLbyFDR.png")

Saving 7 x 5 in image

#!/bin/rscripts


#this script has no arguments, it will take the nuclear and total results then output qqplots of these results overlaped with the other molecular QTLs 

library(dplyr)
library(scales)


#import other QTLs  

QTL_names=c("gene", "snpID","distance", "pval", "slope")

fourSU30= read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_4su30.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)

fourSU60=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_4su60.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)

RNAseq=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)

guevardis=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseqGeuvadis.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)

ribo=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)

prot=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)


#import nuc and tot results  
res_names=c("peakID", "snpID", "dist", "res.pval", "slope")

nuc.nom=read.table("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", header = F, col.names = res_names, stringsAsFactors = F)


tot.nom=read.table("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", header = F, col.names = res_names, stringsAsFactors = F)



#subset total  

fourSU30AndTot= fourSU30 %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
fourSU30_unif_T=runif(nrow(fourSU30AndTot))

fourSU60AndTot= fourSU60 %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
fourSU60_unif_T=runif(nrow(fourSU60AndTot))


RNAAndTot= RNAseq %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
RNAseq_unif_T=runif(nrow(RNAAndTot))


GuevAndTot= guevardis %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
guev_unif_T=runif(nrow(GuevAndTot))


riboAndTot= ribo %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
ribo_unif_T=runif(nrow(riboAndTot))

protAndTot= prot %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
prot_unif_T=runif(nrow(protAndTot))

#subset nuc

fourSU30AndNuc= fourSU30 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
fourSU30_unif_N=runif(nrow(fourSU30AndNuc))

fourSU60AndNuc= fourSU60 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
fourSU60_unif_N=runif(nrow(fourSU60AndNuc))


RNAAndNuc= RNAseq %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
RNAseq_unif_N=runif(nrow(RNAAndNuc))


GuevAndNuc= guevardis %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
guev_unif_N=runif(nrow(GuevAndNuc))


riboAndNuc= ribo %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
ribo_unif_N=runif(nrow(riboAndNuc))

protAndNuc= prot %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
prot_unif_N=runif(nrow(protAndNuc))


#plot res
##nuclear
png('/project2/gilad/briana/threeprimeseq/output/nuc.allQTLs.png')
qqplot(-log10(runif(nrow(nuc.nom))), -log10(nuc.nom$res.pval),ylab="-log10 Nuclear nominal pvalue", xlab="Uniform expectation", main="Nuclear Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif_N)), sort(-log10(fourSU30AndNuc$res.pval)), col= alpha("Red", 0.3))
points(sort(-log10(fourSU60_unif_N)), sort(-log10(fourSU60AndNuc$res.pval)), col=alpha("Orange",.3))
points(sort(-log10(RNAseq_unif_N)), sort(-log10(RNAAndNuc$res.pval)), col=alpha("Yellow",.3))
points(sort(-log10(guev_unif_N)), sort(-log10(GuevAndNuc$res.pval)), col=alpha("Green",.3))
points(sort(-log10(ribo_unif_N)), sort(-log10(riboAndNuc$res.pval)), col=alpha("Blue", .3))
points(sort(-log10(prot_unif_N)), sort(-log10(protAndNuc$res.pval)), col=alpha("Purple",.3))
abline(0,1)
legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Protein"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)
dev.off()


##total
png('/project2/gilad/briana/threeprimeseq/output/tot.allQTLs.png')
qqplot(-log10(runif(nrow(tot.nom))), -log10(tot.nom$res.pval),ylab="-log10 Total nominal pvalue", xlab="Uniform expectation", main="Total Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif_T)), sort(-log10(fourSU30AndTot$res.pval)), col= alpha("Red", 0.3))
points(sort(-log10(fourSU60_unif_T)), sort(-log10(fourSU60AndTot$res.pval)), col=alpha("Orange",.3))
points(sort(-log10(RNAseq_unif_T)), sort(-log10(RNAAndTot$res.pval)), col=alpha("Yellow",.3))
points(sort(-log10(guev_unif_T)), sort(-log10(GuevAndTot$res.pval)), col=alpha("Green",.3))
points(sort(-log10(ribo_unif_T)), sort(-log10(riboAndTot$res.pval)), col=alpha("Blue", .3))
points(sort(-log10(prot_unif_T)), sort(-log10(protAndTot$res.pval)), col=alpha("Purple",.3))
abline(0,1)
legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Protein"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)
dev.off()

#!/bin/bash

#SBATCH --job-name=run_overlapQTL_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_overlapQTL_transcript.out
#SBATCH --error=run_overlapQTL_transcript.err
#SBATCH --partition=bigmem2
#SBATCH --mem=64G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

Rscript overlap_QTLplots_Trans.R 

tot.perm= tot.perm %>% mutate(sig=ifelse( -log10(bh) >= 1 , "Yes", "No"))
tot.perm$sig=as.factor(tot.perm$sig)

totQTLdist_plot= ggplot(tot.perm, aes(x=log10(abs(dist)), by=sig, fill=sig)) + geom_density(alpha=.5) + labs(title="Distance between snp and peak\n Total fraction")

nuc.perm= nuc.perm %>% mutate(sig=ifelse( -log10(bh) >= 1 , "Yes", "No"))
nuc.perm$sig=as.factor(nuc.perm$sig)

nucQTLdist_plot= ggplot(nuc.perm, aes(x=log10(abs(dist)), by=sig, fill=sig)) + geom_density(alpha=.5) + labs(title="Distance between snp and peak\n Nuclear fraction")

plot_grid(totQTLdist_plot, nucQTLdist_plot )

tot.perm_sigOnly=tot.perm %>% filter(sig=="Yes")
nuc.perm_sigOnly=nuc.perm %>% filter(sig=="Yes")

#nuc in total 
nuc.perm_sigOnly_inT= nuc.perm_sigOnly %>% semi_join(tot.perm_sigOnly, by=c("sid", "pid")) 
nrow(nuc.perm_sigOnly_inT)

[1] 22

nuc.perm_sigOnly_notT= nuc.perm_sigOnly %>% anti_join(tot.perm_sigOnly, by=c("sid", "pid"))
nrow(nuc.perm_sigOnly_notT)

[1] 858

#total in nuc 
tot.perm_sigOnly_inT= tot.perm_sigOnly %>% semi_join(nuc.perm_sigOnly,  by=c("sid", "pid"))
nrow(tot.perm_sigOnly_inT)

[1] 22

tot.perm_sigOnly_notT= tot.perm_sigOnly %>% anti_join(nuc.perm_sigOnly,  by=c("sid", "pid"))
nrow(tot.perm_sigOnly_notT)

[1] 96

grid.newpage()
qtloverlap=draw.pairwise.venn(area1 = 3049, area2 = 677, cross.area = 148, category = c("Nuclear: QTLs", "Total: QTLs"), lty = rep("solid", 2), fill = c("light blue", "pink"), alpha = rep(0.5, 2), cat.pos = c(0, 0), cat.dist = rep(0.025, 2))

#nuc genes
nuc.perm_sigOnly_gene= nuc.perm_sigOnly %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(gene) %>% distinct(gene)
nrow(nuc.perm_sigOnly_gene)

[1] 715

#total genes
tot.perm_sigOnly_gene= tot.perm_sigOnly %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(gene) %>% distinct(gene)
nrow(tot.perm_sigOnly_gene)  

[1] 106

nuc.perm_sigOnly_gene %>% semi_join(tot.perm_sigOnly_gene, by="gene") %>% nrow()

[1] 48

nuc.perm_sigOnly_gene %>% anti_join(tot.perm_sigOnly_gene, by="gene") %>% nrow()

[1] 667

tot.perm_sigOnly_gene %>% semi_join(nuc.perm_sigOnly_gene, by="gene") %>% nrow()

[1] 48

tot.perm_sigOnly_gene %>% anti_join(nuc.perm_sigOnly_gene, by="gene") %>% nrow()

[1] 58

grid.newpage()
png("../output/plots/geneswithAPAQTL.ven.png")
qtloverlap_gene=draw.pairwise.venn(area1 = 2272, area2 = 602, cross.area = 398, category = c("Genes with APAqtls\n Nuclear", "Genes with APAqtls\n Total"), lty = rep("solid", 2), fill = c("light blue", " purple"), alpha = rep(0.5, 2), cat.pos = c(0, 26), cat.dist = c(0.03, 0.03))
dev.off()

quartz_off_screen 
                2 

Use these phenotypes for Diff Iso Analysis

Run on counts:

I need to run feature counts on all of the data so the total and nuclear files are in the same file

ref_gene_peakTranscript_fc.sh

#!/bin/bash

#SBATCH --job-name=ref_gene_peakTranscript_fc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakTranscript_fc.out
#SBATCH --error=ref_gene_peakTranscript_fc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 2

fix_head_fc_trans.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries = i_list[:6]
        print(libraries)
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

fc2leafphen_transcript.py

inFile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_fixed.fc", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_forLC.fc", "w")

for num, ln in enumerate(inFile):
        if num == 1:
            lines=ln.split()[6:]
            outFile.write(" ".join(lines)+'\n')
        if num > 1:
            ID=ln.split()[0]
            peak=ID.split(":")[0]
            chrom=ID.split(":")[1]
            start=ID.split(":")[2]
            start=int(start)
            end=ID.split(":")[3]
            end=int(end)
            strand=ID.split(":")[4]
            gene=ID.split(":")[5]
            new_ID="chr%s:%d:%d:%s"%(chrom, start, end, gene)
            pheno=ln.split()[6:]
            pheno.insert(0, new_ID)
            outFile.write(" ".join(pheno)+'\n')
            
outFile.close()  

subset_diffisopheno_transcript.py

def main(inFile, outFile, target):
    ifile=open(inFile, "r")
    ofile=open(outFile, "w")
    target=int(target)
    for num, ln in enumerate(ifile):
        if num == 0:
            ofile.write(ln)
        else:
            ID=ln.split()[0]
            chrom=ID.split(":")[0][3:]
            print(chrom)
            chrom=int(chrom)
            if chrom == target:
                ofile.write(ln)
            
if __name__ == "__main__":
    import sys

    target = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_forLC.fc"
    outFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.ALL.pheno_fixed_%s.txt"%(target)
    main(inFile, outFile, target)

Run this with: run_subset_diffisopheno_transcript.sh

#!/bin/bash

#SBATCH --job-name=run_subset_diffisopheno_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_subset_diffisopheno_transcript.out
#SBATCH --error=run_subset_diffisopheno_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
python subset_diffisopheno_transcript.py $i 
done

Make a samples list script.

MakeDifIsoSampleList_transcript.py

outfile=open("/project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/sample_groups.txt", "w")
infile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc", "r")

for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=[]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        for l in libraries:
            if l[-1] == "T":
                outfile.write("%s\tTotal\n"%(l))
            else:
                outfile.write("%s\tNuclear\n"%(l))
      else:
          next
                
outfile.close()

run_leafcutter_ds_bychrom_tr.sh

#!/bin/bash

#SBATCH --job-name=run_leafcutter_ds_bychrom_tr
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_leafcutter_ds_bychrom_tr.out
#SBATCH --error=run_leafcutter_ds_bychrom_tr.err
#SBATCH --partition=bigmem2
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load R

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
Rscript /project2/gilad/briana/davidaknowles-leafcutter-c3d9474/scripts/leafcutter_ds.R --num_threads 4  /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.ALL.pheno_fixed_${i}.txt /project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/sample_groups.txt -o /project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/TN_diff_isoform_chr${i}.txt 
done

• Error in colSums(cluster_counts > 0): ‘x’ must be an array of at least two dimensions

• Not enough valid samples NA NA NA chr7:TRPV6 NA

• <=1 sample with coverage>min_coverage NA NA NA chr7:WBSCR17 NA

There are duplicates peak IDs in chr 6 and 19. This could be due to the same gene name on diff strands from diff versions of the gene. The problems on 6 come from HLA, the one overlap on 19 is DPP9. I am going to remove the dep lines with low coverage because they will probably drop out of the leafcutter analysis due to low numbers.

The errors in the significance files are due to clusters that do not satisfy requirements for leafcutter. Either there is only 1 peak in the gene, there are not enought samples with coverage or the min coverage is not satisfied. I can remove these peaks from the results.

Plot results:

diffIso=read.table("../data/diff_iso_trans/TN_diff_isoform_all_cluster_sig_Succ.txt", col.names = c("status",   "loglr",    "df",   "p",    "cluster",  "p.adjust"))

qqplot(-log10(runif(nrow(diffIso))), -log10(diffIso$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
abline(0,1)

Past versions of unnamed-chunk-54-1.png

A better way to look at this is effect sizes because we expect a large amount of signal here.

effectsize=read.table("../data/diff_iso_trans/TN_diff_isoform_ALL.txt_effect_sizes.fixed.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltapsi'))

Plot effect sizes:

effectsize$logef=as.numeric(effectsize$logef)
plot(sort(effectsize$logef),main="Leafcutter effect Sizes", ylab="Effect size", xlab="Peak Index")

Past versions of unnamed-chunk-56-1.png

Negative effect sizes are more in nuclear. There are 193842 negative effect sizes and 70873 positive.

I want to color this plot by top and bottom 5%.

quantile(effectsize$logef,na.rm=T,probs = seq(0, 1, .05))

          0%           5%          10%          15%          20% 
-20.08363095  -0.60972092  -0.45829291  -0.36423672  -0.29749657 
         25%          30%          35%          40%          45% 
 -0.25078432  -0.21370090  -0.18275473  -0.15592521  -0.13132430 
         50%          55%          60%          65%          70% 
 -0.11103719  -0.09127381  -0.07203322  -0.05144509  -0.02853051 
         75%          80%          85%          90%          95% 
  0.04114049   0.20084899   0.41743866   0.73096535   1.18251507 
        100% 
  7.45641015 

5% is -.61, 95% is 1.18

effectsize$colorsF=ifelse(effectsize$logef >= 1.18, "darkviolet", ifelse(effectsize$logef <= -.61,"deepskyblue3", "black"))

plot(effectsize$logef, col = effectsize$colorsF ,main="Leafcutter effect Sizes", ylab="Effect size")
legend("bottomleft", legend=c("Top 5%: Total", "Bottom 5%: Nuclear"),
       col=c( "darkviolet","deepskyblue3"), pch=19, cex=0.8)

Past versions of unnamed-chunk-58-1.png

I want to plot this by chr.

effectsize$colorsF=as.factor(effectsize$colorsF)

effectsize_chr=effectsize %>% tidyr::separate(intron, into=c("chrom", "start", "end", "gene"), sep=":")

effectsize_chr$chrom=as.factor(effectsize_chr$chrom)
ggplot(effectsize_chr, aes(x=chrom, y=logef, col=chrom)) + geom_jitter()+ theme(axis.text.x = element_text(angle = 90, hjust = 1))

Past versions of unnamed-chunk-59-1.png

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2      VennDiagram_1.6.20  futile.logger_1.4.3
 [4] reshape2_1.4.3      cowplot_0.9.3       workflowr_1.2.0    
 [7] forcats_0.3.0       stringr_1.4.0       dplyr_0.7.6        
[10] purrr_0.2.5         readr_1.1.1         tidyr_0.8.1        
[13] tibble_1.4.2        ggplot2_3.0.0       tidyverse_1.2.1    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4     haven_1.1.2          lattice_0.20-35     
 [4] colorspace_1.3-2     htmltools_0.3.6      yaml_2.2.0          
 [7] rlang_0.2.2          pillar_1.3.0         glue_1.3.0          
[10] withr_2.1.2          lambda.r_1.2.3       modelr_0.1.2        
[13] readxl_1.1.0         bindr_0.1.1          plyr_1.8.4          
[16] munsell_0.5.0        gtable_0.2.0         cellranger_1.1.0    
[19] rvest_0.3.2          evaluate_0.13        labeling_0.3        
[22] knitr_1.20           broom_0.5.0          Rcpp_0.12.19        
[25] formatR_1.5          scales_1.0.0         backports_1.1.2     
[28] jsonlite_1.6         fs_1.2.6             hms_0.4.2           
[31] digest_0.6.17        stringi_1.2.4        rprojroot_1.3-2     
[34] cli_1.0.1            tools_3.5.1          magrittr_1.5        
[37] lazyeval_0.2.1       futile.options_1.0.1 crayon_1.3.4        
[40] whisker_0.3-2        pkgconfig_2.0.2      MASS_7.3-50         
[43] xml2_1.2.0           lubridate_1.7.4      assertthat_0.2.0    
[46] rmarkdown_1.11       httr_1.3.1           rstudioapi_0.9.0    
[49] R6_2.3.0             nlme_3.1-137         git2r_0.24.0        
[52] compiler_3.5.1